Deep learning based online metallic surface defect detection method for wire and arc additive manufacturing. (April 2023)

• Record Type: Journal Article
• Authors: Li, Wenhao; Zhang, Haiou; Wang, Guilan; Xiong, Gang; Zhao, Meihua; Li, Guokuan; Li, Runsheng
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 80(2023)
• Publication Date: 2023-04
• Subjects: Wire and arc additive manufacturing -- Defect detection -- Online -- Deep learning; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102470
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: Wire and arc additive manufacturing (WAAM) is an emerging manufacturing technology that is widely used in different manufacturing industries. To achieve fully automated production, WAAM requires a dependable, efficient, and automatic defect detection system. Although machine learning is dominant in the object detection domain, classic algorithms have defect detection difficulty in WAAM due to complex defect types and noisy detection environments. This paper presents a deep learning-based novel automatic defect detection solution, you only look once (YOLO)-attention, based on YOLOv4, which achieves both fast and accurate defect detection for WAAM. YOLO-attention makes improvements on three existing object detection models: the channel-wise attention mechanism, multiple spatial pyramid pooling, and exponential moving average. The evaluation on the WAAM defect dataset shows that our model obtains a 94.5 mean average precision (mAP) with at least 42 frames per second. This method has been applied to additive manufacturing of single-pass, multi-pass deposition and parts. It demonstrates its feasibility in practical industrial applications and has potential as a vision-based methodology that can be implemented in real-time defect detection systems. Highlights: Propose a defect detection method in additive manufacturing based on deep learning. Use attention model, multiple spatial pyramid pooling and exponential moving average. Establish a wire and arc additive manufacturing defect dataset to verify the effect. YOLO-attention achieves a mean average precision of 94.5%.